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    Shrinking qubits for quantum computing with atom-thin materials

    For quantum computers to surpass their classical counterparts in speed and capacity, their qubits — which are superconducting circuits that can exist in an infinite combination of binary states — need to be on the same wavelength. Achieving this, however, has come at the cost of size. Whereas the transistors used in classical computers have been shrunk down to nanometer scales, superconducting qubits these days are still measured in millimeters — one millimeter is one million nanometers.
    Combine qubits together into larger and larger circuit chips, and you end up with, relatively speaking, a big physical footprint, which means quantum computers take up a lot of physical space. These are not yet devices we can carry in our backpacks or wear on our wrists.
    To shrink qubits down while maintaining their performance, the field needs a new way to build the capacitors that store the energy that “powers” the qubits. In collaboration with Raytheon BBN Technologies, Wang Fong-Jen Professor James Hone’s lab at Columbia Engineering recently demonstrated a superconducting qubit capacitor built with 2D materials that’s a fraction of previous sizes.
    To build qubit chips previously, engineers have had to use planar capacitors, which set the necessary charged plates side by side. Stacking those plates would save space, but the metals used in conventional parallel capacitors interfere with qubit information storage. In the current work, published on November 18 in Nano Letters, Hone’s PhD students Abhinandan Antony and Anjaly Rajendra sandwiched an insulating layer of boron nitride between two charged plates of superconducting niobium dieselenide. These layers are each just a single atom thick and held together by van der Waals forces, the weak interaction between electrons. The team then combined their capacitors with aluminum circuits to create a chip containing two qubits with an area of 109 square micrometers and just 35 nanometers thick — that’s 1,000 times smaller than chips produced under conventional approaches.
    When they cooled their qubit chip down to just above absolute zero, the qubits found the same wavelength. The team also observed key characteristics that showed that the two qubits were becoming entangled and acting as a single unit, a phenomenon known as quantum coherence; that would mean the qubit’s quantum state could be manipulated and read out via electrical pulses, said Hone. The coherence time was short — a little over 1 microsecond, compared to about 10 microseconds for a conventionally built coplanar capacitor, but this is only a first step in exploring the use of 2D materials in this area, he said.
    Separate work published on arXiv in August from researchers at MIT also took advantage of niobium diselenide and boron nitride to build parallel-plate capacitors for qubits. The devices studied by the MIT team showed even longer coherence times — up to 25 microseconds — indicating that there is still room to further improve performance.
    From here, Hone and his team will continue refining their fabrication techniques and test other types of 2D materials to increase coherence times, which reflect how long the qubit is storing information. New device designs should be able to shrink things down even further, said Hone, by combining the elements into a single van der Waals stack or by deploying 2D materials for other parts of the circuit.
    “We now know that 2D materials may hold the key to making quantum computers possible,” Hone said. “It is still very early days, but findings like these will spur researchers worldwide to consider novel applications of 2D materials. We hope to see a lot more work in this direction going forward.”
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    Materials provided by Columbia University School of Engineering and Applied Science. Original written by Ellen Neff. Note: Content may be edited for style and length. More

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    Time crystal in a quantum computer

    There is a huge global effort to engineer a computer capable of harnessing the power of quantum physics to carry out computations of unprecedented complexity. While formidable technological obstacles still stand in the way of creating such a quantum computer, today’s early prototypes are still capable of remarkable feats.
    For example, the creation of a new phase of matter called a “time crystal.” Just as a crystal’s structure repeats in space, a time crystal repeats in time and, importantly, does so infinitely and without any further input of energy — like a clock that runs forever without any batteries. The quest to realize this phase of matter has been a longstanding challenge in theory and experiment — one that has now finally come to fruition.
    In research published Nov. 30 in Nature, a team of scientists from Stanford University, Google Quantum AI, the Max Planck Institute for Physics of Complex Systems and Oxford University detail their creation of a time crystal using Google’s Sycamore quantum computing hardware.
    “The big picture is that we are taking the devices that are meant to be the quantum computers of the future and thinking of them as complex quantum systems in their own right,” said Matteo Ippoliti, a postdoctoral scholar at Stanford and co-lead author of the work. “Instead of computation, we’re putting the computer to work as a new experimental platform to realize and detect new phases of matter.”
    For the team, the excitement of their achievement lies not only in creating a new phase of matter but in opening up opportunities to explore new regimes in their field of condensed matter physics, which studies the novel phenomena and properties brought about by the collective interactions of many objects in a system. (Such interactions can be far richer than the properties of the individual objects.)
    “Time-crystals are a striking example of a new type of non-equilibrium quantum phase of matter,” said Vedika Khemani, assistant professor of physics at Stanford and a senior author of the paper. “While much of our understanding of condensed matter physics is based on equilibrium systems, these new quantum devices are providing us a fascinating window into new non-equilibrium regimes in many-body physics.”
    What a time crystal is and isn’t More

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    Grouping of immune cell receptors could help decode patients' personal history of infection

    Grouping of pathogen-recognising proteins on immune T cells may be key to identifying if someone has had an infection in the past, suggests a study published today in eLife.
    While tests measuring antibodies against a pathogen are often used to detect signs of a previous infection, it is more difficult for researchers to measure the strength and targets of a person’s T-cell response to infection or vaccination, but the findings hint at a potential new approach. This patient information could one day be useful for detecting infections, guiding treatments or supporting the research and development of new therapies and vaccines.
    Immune T cells help the body find and destroy harmful viruses and bacteria. Proteins on the outer surface of T cells — called receptors — allow the T cells to recognise and eliminate human cells that have been infected by specific pathogens.
    “While the abundance of specific receptors could provide clues about past infection, the enormous molecular diversity of T-cell receptors makes it incredibly challenging to assess which receptors recognise which pathogens. Not only is each pathogen recognised by a distinct set of receptors, but each individual develops a personalised set of receptors for each pathogen,” explains first author Koshlan Mayer-Blackwell, Senior Data Scientist at Fred Hutchinson Cancer Research Center, Seattle, Washington, US. “We developed a new computational approach that allows us to find similarities among pathogen-specific T-cell receptors across individuals. Ultimately, we hope this will help develop signatures of past infection despite the enormous diversity of T-cell receptors.”
    The team tested their approach using data from the immuneRACE study of T-cell receptors in patients with COVID-19. Using their new software for rapidly comparing large sets of receptors, they were able to generate 1,831 T-cell receptor groupings based on similarities in the receptors’ amino acid sequences that suggest they have similar functions.
    In an independent group of COVID-19 patients, the team found that the common molecular patterns associated with receptor groupings were more robustly detected than individual receptor sequences that were previously hypothesised to recognise parts of the SARS-CoV-2 virus, demonstrating a major improvement on existing approaches.
    “Our study introduces and validates a flexible approach to identify sets of similar T-cell receptors, which we hope will be broadly useful for scientists studying T-cell immunity,” Mayer-Blackwell says. “Grouping receptors together in this way makes it possible to compare responses to infection or vaccination across a diverse population.”
    To help other researchers use this approach to develop T-cell biomarkers with their own data, the team has created free customisable software called tcrdist3.
    “Our software provides flexible tools that will enable scientists to analyse and integrate the rapidly growing libraries of T-cell receptor sequencing data that are needed to identify the features of pathogen-specific T-cell receptors,” concludes senior author Andrew Fiore-Gartland, Co-Director of the Vaccines and Immunology Statistical Center at the Fred Hutchinson Cancer Research Center. “We hope it will open new opportunities not only to identify patients’ immunological memories of past infections and vaccinations but also to predict their future immune responses.”
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    Materials provided by eLife. Note: Content may be edited for style and length. More

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    Constraining quantum measurement

    The quantum world and our everyday world are very different places. In a publication that appeared as the “Editor’s Suggestion” in Physical Review A this week, UvA physicists Jasper van Wezel and Lotte Mertens and their colleagues investigate how the act of measuring a quantum particle transforms it into an everyday object.
    Quantum mechanics is the theory that describes the tiniest objects in the world around us, ranging from the constituents of single atoms to small dust particles. This microscopic realm behaves remarkably differently from our everyday experience — despite the fact that all objects in our human-scale world are made of quantum particles themselves. This leads to intriguing physical questions: why are the quantum world and the macroscopic world so different, where is the dividing line between them, and what exactly happens there?
    Measurement problem
    One particular area where the distinction between quantum and classical becomes essential is when we use an everyday object to measure a quantum system. The division between the quantum and everyday worlds then amounts to asking how ‘big’ the measurement device should be to be able to show quantum properties using a display in our everyday world. Finding out the details of measurement, such as how many quantum particles it takes to create a measurement device, is called the quantum measurement problem.
    As experiments probing the world of quantum mechanics become ever more advanced and involve ever larger quantum objects, the invisible line where pure quantum behaviour crosses over into classical measurement outcomes is rapidly being approached. In an article that was highlighted as “Editor’s Suggestion” in Physical Review A this week, UvA physicists Jasper van Wezel and Lotte Mertens and their colleagues take stock of current models that attempt to solve the measurement problem, and particularly those that do so by proposing slight modifications to the one equation that rules all quantum behaviour: Schrödinger’s equation.
    Born’s rule
    The researchers show that such amendments can in principle lead to consistent proposals for solving the measurement problem. However, it turns out to be difficult to create models that satisfy Born’s rule, which tells us how to use Schrödinger’s equation for predicting measurement outcomes. The researchers show that only models with sufficient mathematical complexity (in technical terms: models that are non-linear and non-unitary) can give rise to Born’s rule and therefore have a chance of solving the measurement problem and teaching us about the elusive crossover between quantum physics and the everyday world.
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    Materials provided by Universiteit van Amsterdam. Note: Content may be edited for style and length. More

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    Nonverbal social interactions – even with unfriendly avatars – boost cooperation

    Scientists used animated humanoid avatars to study how nonverbal cues influence people’s behavior. Reported in the Journal of Cognitive Neuroscience, the research offers insight into the brain mechanisms that drive social and economic decision-making.
    The study revealed that participants were more willing to cooperate with animated avatars than with static figures representing their negotiation partners. It also found — somewhat surprisingly — that people were more willing to accept unfair offers from unfriendly avatars than from friendly ones.
    “This work is an extension of previous studies exploring how nonverbal cues influence people’s perceptions of one another,” said Matthew Moore, who led the research at the University of Illinois Urbana-Champaign with psychology professors Florin Dolcos and Sanda Dolcos. The new research was conducted at the U. of I.’s Beckman Institute for Advanced Science and Technology, where Moore was a postdoctoral fellow.
    “Nonverbal interactions represent a huge part of human communication,” Sanda Dolcos said. “We might not be aware of this, but much of the information that we take in is through these nonverbal channels.”
    Previous studies often used still photos or other static representations of people engaged in social interactions to study how people form opinions or make decisions, Florin Dolcos said.
    “By animating the avatars, we’re capturing interactions that are much closer to what happens in real-life situations,” he said. More

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    Quantum computers getting connected

    A promising route towards larger quantum computers is to orchestrate multiple task-optimised smaller systems. To dynamically connect and entangle any two systems, photonic interference emerges as a powerful method, due to its compatibility with on-chip devices and long-distance propagation in quantum networks.
    One of the main obstacles towards the commercialization of quantum photonics remains the nanoscale fabrication and integration of scalable quantum systems due to their notorious sensitivity to the smallest disturbances in the close environment. This has made it an extraordinary challenge to develop systems that can be used for quantum computing while simultaneously offering an efficient optical interface.
    A recent result published in Nature Materials shows how the integration obstacle can be overcome. The work is based on a multi-national collaboration with researchers from Universities of Stuttgart (Physics 3), California — Davis, Linköping and Kyoto, as well as the Fraunhofer Institute at Erlangen, the Helmholtz Centre at Dresden and the Leibniz-Institute at Leipzig.
    The researchers followed a two-step approach. First, their quantum system of choice is the so-called silicon vacancy centre in silicon carbide, which is known to possess particularly robust spin-optical properties. Second, they fabricated nanophotonic waveguides around these colour centres using gentle processing methods that keep the host material essentially free of damage.
    “With our approach, we could demonstrate that the excellent spin-optical properties of our colour centres are maintained after nanophotonic integration.” says Florian Kaiser, Assistant Professor at the University of Stuttgart, the supervisor of this project. “Thanks to the robustness of our quantum devices, we gained enough headroom to perform quantum gates on multiple nuclear spin qubits. As these spins show very long coherence times, they are excellent for implementing small quantum computers.”
    “In this project, we explored the peculiar triangular shape of photonic devices. While this geometry is of commercial appeal because it provides versatility needed for scalable production, little has been known about its utility for high performing quantum hardware. Our studies reveal that light emitted by the colour centre, which carries quantum information across the chip, can be efficiently propagated through a single optical mode. This is a key conclusion for viability of integration of colour centres with other photonic devices, such as nanocavities, optical fibre and single-photon detectors, needed to realize full functionalities of quantum networking and computing.” — says Marina Radulaski, Assistant Professor at the University of California — Davis.
    What makes the silicon carbide platform particularly interesting are its CMOS compatibility and its heavy usage as high-power semiconductor in electric mobility. The researchers now want to benefit from these aspects to leverage the scalable production of spin-photonics chips. Additionally, they want to implement semiconductor circuitry to electrically initialise and readout the quantum states of their spin qubits. “Maximising electrical control — instead of traditional optical control via lasers — is an important step towards system simplification. The combination of efficient nanophotonics with electrical control will allow us to reliably integrate more quantum systems on one chip, which will result in significant performance gains.,” adds Florian Kaiser, “In this sense, we are only at the dawn of quantum technologies with colour centres in silicon carbide. Our successful nanophotonic integration is not only an exciting enabler for distributed quantum computing, but it can also boost the performance of compact quantum sensors.”
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    Materials provided by Universitaet Stuttgart. Note: Content may be edited for style and length. More

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    Artificial intelligence that understands object relationships

    When humans look at a scene, they see objects and the relationships between them. On top of your desk, there might be a laptop that is sitting to the left of a phone, which is in front of a computer monitor.
    Many deep learning models struggle to see the world this way because they don’t understand the entangled relationships between individual objects. Without knowledge of these relationships, a robot designed to help someone in a kitchen would have difficulty following a command like “pick up the spatula that is to the left of the stove and place it on top of the cutting board.”
    In an effort to solve this problem, MIT researchers have developed a model that understands the underlying relationships between objects in a scene. Their model represents individual relationships one at a time, then combines these representations to describe the overall scene. This enables the model to generate more accurate images from text descriptions, even when the scene includes several objects that are arranged in different relationships with one another.
    This work could be applied in situations where industrial robots must perform intricate, multistep manipulation tasks, like stacking items in a warehouse or assembling appliances. It also moves the field one step closer to enabling machines that can learn from and interact with their environments more like humans do.
    “When I look at a table, I can’t say that there is an object at XYZ location. Our minds don’t work like that. In our minds, when we understand a scene, we really understand it based on the relationships between the objects. We think that by building a system that can understand the relationships between objects, we could use that system to more effectively manipulate and change our environments,” says Yilun Du, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.
    Du wrote the paper with co-lead authors Shuang Li, a CSAIL PhD student, and Nan Liu, a graduate student at the University of Illinois at Urbana-Champaign; as well as Joshua B. Tenenbaum, the Paul E. Newton Career Development Professor of Cognitive Science and Computation in the Department of Brain and Cognitive Sciences and a member of CSAIL; and senior author Antonio Torralba, the Delta Electronics Professor of Electrical Engineering and Computer Science and a member of CSAIL. The research will be presented at the Conference on Neural Information Processing Systems in December. More

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    Team builds first living robots that can reproduce

    To persist, life must reproduce. Over billions of years, organisms have evolved many ways of replicating, from budding plants to sexual animals to invading viruses.
    Now scientists at the University of Vermont, Tufts University, and the Wyss Institute for Biologically Inspired Engineering at Harvard University have discovered an entirely new form of biological reproduction — and applied their discovery to create the first-ever, self-replicating living robots.
    The same team that built the first living robots (“Xenobots,” assembled from frog cells — reported in 2020) has discovered that these computer-designed and hand-assembled organisms can swim out into their tiny dish, find single cells, gather hundreds of them together, and assemble “baby” Xenobots inside their Pac-Man-shaped “mouth” — that, a few days later, become new Xenobots that look and move just like themselves.
    And then these new Xenobots can go out, find cells, and build copies of themselves. Again and again.
    “With the right design — they will spontaneously self-replicate,” says Joshua Bongard, Ph.D., a computer scientist and robotics expert at the University of Vermont who co-led the new research.
    The results of the new research were published November 29, 2021, in the Proceedings of the National Academy of Sciences.
    Into the Unknown More